Peningkatan Kualitas Citra Reduksi Noise Menggunakan Iterative Denoising and Backward Projection-CNN dan TFM-CLAHE Pada Citra 24 Bit

Penurunan kualitas yang diakibatkan adanya noise atau kontras yang tidak normal pada citra mengakibatkan objek pada citra menjadi tidak jelas. Masalah itu dapat disebabkan perangkat yang digunakan menimbulkan noise atau tidak bisa menghasilkan kontras yang normal. Adanya noise dan kontras rendah gelap berdampak besar terhadap kualitas citra?, proses reduksi noise yang berukuran besar 45% akan berpengaruh pada informasi didalam citra sehingga kualitas citra hasil reduksi menjadi hal yang perlu dipertimbangkan untuk noise berukuran besar?. Penelitian tahun 2019 menggunakan algoritma Iterative Denoising and Backward Projections with CNN (IDBP-CNN) dinyatakan mampu mereduksi noise hingga 51% dengan kualitas PSNR diatas 30 dB dengan mengabaikan kontras dari citra. Sedangkan algoritma untuk meningkatkan kontras citra menggunakan algoritma Triangular Fuzzy Membership?Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) juga diklaim mampu meningkatkan kontras citra dengan kualitas PSNR di atas 20 dB, yang lebih baik dibandingkan dengan algoritma CLAHE. Berdasarkan hasil pengujian yang dilakukan pada 10 citra kontras rendah gelap dengan noise 45% didapatkan kombinasi algoritma TFM-CLAHE diikuti IDBP-CNN lebih baik dengan rata-rata hasil PSNR = 31.69 dB, dibandingkan sebaliknya PSNR = 31.01 dB, Namun rata-rata keragaman informasi citra hasil dengan kombinasi IDBP-CNN diikuti TFM-CLAHE lebih kecil selisihnya terhadap citra asli berdasarkan Shanon Entropy sebesar 3.77% dibandingkan sebaliknya 4.75%

Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

Film video noise can usually be defined as the error information visible on the video image, caused by the digital signal system. This distortion is inevitably present in the video obtained by various camera equipment. Noise reduction techniques are important preprocessing processes in many video processing applications, and its main goal is to reduce the noise contained in a video image while preserving as much of its edge and texture information as possible. In this paper, we describe in detail the principles of the space-time noise reduction filter, propose a 3D-filter algorithm for Gaussian noise, an improved 3D-filter algorithm based on the 3D-BDP (bloom-deep-split) filter for mixed noise, and a filter algorithm for luminance and color noise in low-brightness scenes. By dissecting the partial differential equation (PDE) denoising process, we establish a new iterative denoising algorithm. The partial differential equation method can be considered as the iterative denoising of the filter, and the first stage of the new algorithm uses wavelet-domain adaptive Wiener filter as the filtering base and achieves good results by adjusting the parameters. The proposed model in this paper is compared with the existing denoising model, and the analysis results show that the model proposed in this section can effectively remove multiplicative noise. The experimental report shows that the parameters set by the algorithm have some stability and can achieve good processing results for multiple images, which is an advantage over the partial differential equation method for denoising. The second stage of the algorithm uses the appropriate partial differential equation method to remove the pseudo-Gibbs in the first stage, which further improves the performance of the algorithm. After the image containing Gaussian noise is processed by the new algorithm, the pseudo-Gibbs effect, which often occurs in wavelet denoising, is eliminated, and the step effect, which occurs in partial differential equation denoising, is avoided; the details are better preserved, and the peak signal-to-noise ratio is improved, and a large number of experiments show that it is an effective denoising method.