soumenca / imageprocessing_in_matlab

Image Processing Algorithms Implemented in Matlab

Image Processing in Matlab

1.Write a program to implement histogram equalization. Capture a photograph in dark. No light (or less light) should be visible in the photograph. Then, apply your histogram equalization program on all the 3 channels (R,G,B) of your dark image, and show the result.

2.Write a program to take the same image as input, and apply gamma transformation on all the 3 channels. Show the results for taking (a) gamma as 5 and (b) gamma as 0.2.

3.Write a program to smoothen a greyscale image by a 3X3 smoothing filter, which emphasizes on the current pixel value, gives lesser weightage to its 4-neighbors and much lesser weightage to the 8-neighbor pixels. Compare the result with median filter.

4.Write a program to sharpen the same image by (a) gradient and (b) Laplacian and compare the results.

5.Write a program to transform a greyscale image to frequency domain by Fourier transform. Apply any three low-pass filters on it and transform back each of the results to spatial domain and display the result images.

6.Write a program to transform a greyscale image to frequency domain by Fourier transform. Apply any three high-pass filters on it and transform back each of the results to spatial domain and display the result images.

7.Take an RGB color image and obtain the histograms of the image separately in Hue, Saturation and Intensity channels.

8.Take a grayscale image and map the image by applying run-length encoding technique to reduce the spatial redundancy. Then apply modified Huffman coding technique (column-wise) to compress the image. Apply reverse procedure to get back the image and display the difference image.

9.Take a grayscale image and apply Haar transform. For ease in computation resize the input image by downsampling.