Image inpainting concept in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of image inpainting?

Imagine you have a photo with some parts missing or damaged. What does image inpainting try to do with that photo?

A Detect objects in the photo and label them.

B Increase the resolution of the entire photo to make it clearer.

C Change the colors of the photo to make it look artistic.

D Fill in the missing or damaged parts with plausible content that matches the surrounding area.

💡 Hint

Think about fixing or restoring parts of an image.

Image inpainting is about filling missing or damaged areas in an image with content that looks natural and consistent with the rest of the image.

❓ Model Choice

intermediate

Which type of model is commonly used for image inpainting?

You want to build a model that fills missing parts of images realistically. Which model type is best suited for this?

A Convolutional Neural Network (CNN) with encoder-decoder architecture

B Recurrent Neural Network (RNN) for sequence prediction

C Support Vector Machine (SVM) for classification

D K-Nearest Neighbors (KNN) for clustering

💡 Hint

Think about models good at understanding images and generating pixels.

CNNs with encoder-decoder structures are widely used for image inpainting because they can learn to understand image context and generate missing pixels.

❓ Predict Output

advanced

What is the output shape after inpainting with this PyTorch model?

Given the following PyTorch code snippet for an image inpainting model, what is the shape of the output tensor?

Computer Vision

import torch
import torch.nn as nn

class SimpleInpaint(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(64, 3, 2, stride=2),
            nn.Sigmoid()
        )
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

model = SimpleInpaint()
input_tensor = torch.randn(1, 3, 128, 128)
output = model(input_tensor)
output.shape

A torch.Size([1, 3, 128, 128])

B torch.Size([1, 64, 64, 64])

C torch.Size([1, 3, 64, 64])

D torch.Size([1, 64, 128, 128])

💡 Hint

Check how MaxPool2d and ConvTranspose2d change spatial dimensions.

MaxPool2d with kernel 2 halves the height and width from 128 to 64. ConvTranspose2d with kernel 2 and stride 2 doubles it back to 128. The output channels are 3, matching input channels.

❓ Metrics

advanced

Which metric best measures the quality of image inpainting results?

You want to evaluate how well your inpainting model fills missing parts of images. Which metric is most suitable?

A F1 Score

B Peak Signal-to-Noise Ratio (PSNR)

C Mean Squared Error (MSE) on class labels

D Accuracy

💡 Hint

Think about metrics that compare images pixel-by-pixel.

PSNR measures the similarity between the original and inpainted images, indicating reconstruction quality. Accuracy and F1 are for classification, not image similarity.

🔧 Debug

expert

Why does this image inpainting model produce blurry results?

You trained a CNN-based image inpainting model, but the filled areas look blurry and lack details. What is the most likely cause?

A The input images were too large, causing memory overflow.

B The model uses too many convolutional layers, causing overfitting.

C The model was trained using only pixel-wise loss like Mean Squared Error, which encourages averaging and smooth outputs.

D The model uses batch normalization, which reduces sharpness.

💡 Hint

Think about how loss functions affect output sharpness.

Pixel-wise losses like MSE penalize differences equally, leading the model to produce average pixel values in uncertain areas, causing blur. Using adversarial or perceptual losses can improve sharpness.

Practice

1. What is the main purpose of image inpainting in computer vision?

easy

A. To fill missing or damaged parts of an image

B. To increase the resolution of an image

C. To classify objects in an image

D. To detect edges in an image

Solution

1. Step 1: Understand image inpainting definition

   Image inpainting is a technique used to restore or fill missing or damaged parts of an image.

2. Step 2: Compare options with definition

   Only To fill missing or damaged parts of an image matches the purpose of filling missing or damaged parts, while others describe different tasks.

3. Final Answer:

   To fill missing or damaged parts of an image -> Option A

4. Quick Check:

   Image inpainting = fill missing parts [OK]

Hint: Remember: inpainting means filling gaps in images [OK]

Common Mistakes:

• Confusing inpainting with image classification
• Thinking it increases image resolution
• Mixing it up with edge detection

2. Which of the following is the correct way to represent a mask for image inpainting?

easy

A. A binary image where 1 indicates missing pixels and 0 indicates known pixels

B. A color image with random colors

C. A text file describing image pixels

D. A grayscale image where white pixels mark missing areas

Solution

1. Step 1: Understand mask role in inpainting

   The mask guides the model where to fill missing parts, usually as a binary image.

2. Step 2: Identify correct mask format

   A binary image where 1 indicates missing pixels and 0 indicates known pixels correctly describes a binary mask with 1 for missing and 0 for known pixels.

3. Final Answer:

   A binary image where 1 indicates missing pixels and 0 indicates known pixels -> Option A

4. Quick Check:

   Mask = binary image marking missing areas [OK]

Hint: Mask uses 1 for missing, 0 for known pixels [OK]

Common Mistakes:

• Thinking mask is a color image
• Assuming mask is a text file
• Confusing grayscale with binary mask

3. Given this Python snippet for image inpainting using OpenCV:

import cv2
image = cv2.imread('photo.jpg')
mask = cv2.imread('mask.png', 0)
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
print(inpainted.shape)

What will be the output if photo.jpg is 640x480 pixels with 3 color channels?

medium

A. (640, 480, 3)

B. (480, 640, 3)

C. (480, 640)

D. (640, 480)

Solution

1. Step 1: Understand image shape format in OpenCV

   OpenCV images have shape (height, width, channels). For 640x480, height=480, width=640.

2. Step 2: Confirm inpainted image shape

   Inpainting returns an image of same size and channels as input, so shape is (480, 640, 3).

3. Final Answer:

   (480, 640, 3) -> Option B

4. Quick Check:

   OpenCV shape = (height, width, channels) [OK]

Hint: OpenCV shape order is height, width, channels [OK]

Common Mistakes:

• Swapping width and height in shape
• Ignoring color channels in output
• Assuming grayscale output

4. You run this code but get an error:

import cv2
image = cv2.imread('photo.jpg')
mask = cv2.imread('mask.png')
inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_NS)

What is the likely cause of the error?

medium

A. Inpaint radius is too large

B. Image file path is incorrect

C. Using wrong inpainting method

D. Mask image is not loaded as a single channel (grayscale)

Solution

1. Step 1: Check mask loading method

   The mask must be loaded as grayscale (single channel) for inpainting, but code loads it as color by default.

2. Step 2: Identify cause of error

   Passing a 3-channel mask causes cv2.inpaint to fail, so loading mask with flag 0 is needed.

3. Final Answer:

   Mask image is not loaded as a single channel (grayscale) -> Option D

4. Quick Check:

   Mask must be grayscale for inpainting [OK]

Hint: Load mask with cv2.imread('mask.png', 0) [OK]

Common Mistakes:

• Loading mask as color image
• Ignoring file path errors
• Changing inpainting method unnecessarily

5. You want to remove a small object from a photo using image inpainting. Which steps should you follow?

hard

A. Crop the image to exclude the object

B. Blur the entire image to hide the object

C. Create a binary mask marking the object, then apply inpainting with that mask

D. Convert the image to grayscale and apply thresholding

Solution

1. Step 1: Mark the object with a mask

   Create a binary mask where the object pixels are 1 (missing) and others 0 (known).

2. Step 2: Apply inpainting using the mask

   Use an inpainting algorithm with the mask to fill the object area with surrounding pixels.

3. Final Answer:

   Create a binary mask marking the object, then apply inpainting with that mask -> Option C

4. Quick Check:

   Mask object + inpaint = object removal [OK]

Hint: Mask object area before inpainting to remove it [OK]

Common Mistakes:

• Blurring instead of inpainting
• Cropping loses image context
• Thresholding unrelated to object removal