NOVEL APPROACH FOR DENOISING USING HADOOP IMAGE PROCESSING INTERFACE
Nidhi Sharma, Sachin Bagga, Akshay Girdhar · Procedia Computer Science · 2018
Noise Reduction and Optimize of Resources at each level of the supply chain is a hard optimization problem especially, where knowledge base generated from large datasets. Digital Image Processing and Image Data Science are prime areas where optimization and noisy data sets problems are occurred due to mutation, the uncertainty of input, and versatility of nature. Noise Reduction Algorithm provides the modulate solution for optimization problems related to image processing industry. The proposed work (HIPID: Hadoop Image Processing Interface Denoising) in which MapReduce tasks work on In-Situ computations in which computations are moving towards the data which reduces the execution time of copying data again and again. The Power of HIPI increases when OpenCV added into it and able to work with images and image enhancement using multiple operations can apply to massive images. HIPI starts with culling stage in which area of searching some specified type of images get reduced and then pass to the next stage mapping, mappers get activated on images randomly and then shuffling and sorting done. In the last consolidated and final output produced by reducer phase. The purposed work includes the study of distributed systems, noise reduction techniques in image processing to reduce the noise in images by an adaptive filter with the optimization. Further purposed approach applies to HIPI environment to analyze the execution time, PSNR and MSE of images, to achieve better PSNR for qualitative visual outputs.