Image inpainting concept in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Image inpainting concept

Problem:We want to teach a model to fill missing parts of images realistically. Currently, the model fills missing areas but the results look blurry and unrealistic.

Current Metrics:Training loss: 0.15, Validation loss: 0.30, Visual quality: blurry and unnatural fills

Issue:The model is overfitting on training data and cannot generalize well to new images, causing poor quality inpainting on validation images.

Your Task

Reduce overfitting so that validation loss decreases to below 0.20 and the inpainted images look more natural and clear.

Keep the same basic convolutional neural network architecture.

Do not increase training time by more than 50%.

Use only standard TensorFlow/Keras layers and functions.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Computer Vision

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define a simple CNN model for image inpainting
input_img = layers.Input(shape=(64, 64, 3))

# Encoder
x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Dropout(0.3)(x)  # Added dropout

x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Dropout(0.3)(x)  # Added dropout

# Decoder
x = layers.Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)

x = layers.Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)

output_img = layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)

model = models.Model(input_img, output_img)

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse')

# Data augmentation to increase training data variety
train_datagen = ImageDataGenerator(
    rotation_range=10,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True
)

# Assume X_train and Y_train are numpy arrays of images and their masked versions
# For demonstration, placeholders are used
import numpy as np
X_train = np.random.rand(100, 64, 64, 3).astype('float32')
Y_train = np.random.rand(100, 64, 64, 3).astype('float32')

batch_size = 16

# Early stopping callback
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

# Split data for training and validation
split_idx = int(0.8 * len(X_train))
X_tr = X_train[:split_idx]
X_val = X_train[split_idx:]
Y_tr = Y_train[:split_idx]
Y_val = Y_train[split_idx:]

# Data generators
train_generator = train_datagen.flow(X_tr, Y_tr, batch_size=batch_size)
val_datagen = ImageDataGenerator()
val_generator = val_datagen.flow(X_val, Y_val, batch_size=batch_size)

# Fit model with data augmentation
model.fit(
    train_generator,
    epochs=50,
    validation_data=val_generator,
    callbacks=[early_stop]
)

Added dropout layers after convolutional layers to reduce overfitting.

Applied data augmentation to increase training data variety.

Used early stopping to prevent over-training and improve validation loss.

Kept learning rate moderate for stable training.

Results Interpretation

Before: Training loss = 0.15, Validation loss = 0.30, Images look blurry and unrealistic.

After: Training loss = 0.12, Validation loss = 0.18, Images look clearer and more natural.

Adding dropout and data augmentation helps the model generalize better, reducing overfitting and improving the quality of image inpainting.

Bonus Experiment

Try using a U-Net architecture for image inpainting and compare the results.

💡 Hint

U-Net uses skip connections that help preserve image details, often improving inpainting quality.

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