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Ganorkar, S. R.
- Color Image Enhancement using Discrete Wavelet Transform
Authors
1 Sinhgad college of Engineering, Vadagaon (BK), Pune, IN
2 Department of E&TC, IN
Source
Digital Image Processing, Vol 4, No 14 (2012), Pagination: 801-806Abstract
Noise filters tend to blur image detail, while filters for image sharpening tend to increase noise. So, cascading the two filters does not always give the best performance. We present an integrated filter that reduces noise or sharpens details in a noisy signal, depending on local image statistics. Most traditional noise reduction methods tend to oversuppress high-frequency details. For overcoming this problem we first decompose the input image into flat and edge regions,and remove noise using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. After removing noise in the flat region, we further remove noise in edge regions by adaptively shrinking wavelet coefficients based on the entropy. Moreover, we present a new directional transform using wavelet basis and Gaussian low pass filters. The wavelet coefficients of edge regions are inverse transformed by using the filtered wavelet bases. Experimental results show the proposed algorithm can reduce noise without losing sharp details and is suitable for commercial low-cost imaging systems, such as digital cameras and surveillance system.
Keywords
Entropy Analysis, Image Denoising, Noise Reduction, Wavelet Transform.
- A Detection of Breast Cancer Using Digital Image Processing Techniques
Authors
Source
Digital Image Processing, Vol 10, No 8 (2018), Pagination: 141-143Abstract
Breast cancer is one of the most common cancer among women in India. Early detection of microcalcification cells is very important stage for the further treatment. Calcification is the deposit of calcium in breast tissues. It is helpful to classify benign or malignant. Early detection of cancer depends on the quality of images and the ability of radiologists to read the mammogram images.
In this proposed work, the mammogram images are initially preprocessed using different methods. In this, noise in the background will be removed using median filter, artifacts will be removed using thresholding method and contrast enhancement will be done using contrast limited adaptive histogram equalization techniques.
Then the region of interest will be determined using segmentation by Otsu’s thresholding algorithm. Features of the mammogram images will be extracted using wavelet transform and to determine the information from the images Support Vector Machine classifier will be used.