FPGA design of FFT based intelligent accelerator with optimized Wallace tree multiplier for image super resolution and quality enhancement

Abstract

The super-resolution (SR) method has been widely used to improve the resolution and quality of natural images, and it is currently being employed in medical imaging. Recently, many techniques have been introduced by researchers to improve the resolution and quality of the image. However, these approaches generate blurring and jagged edges in images. Hence, this article introduces novel deep learning (DL) with a fast Fourier transform (FFT) based intelligent approach (IA) to improve the quality and resolution of the image. A convolutional neural network (CNN) is emphasized for quality enhancement and resolution to recover lost information during image acquisition. This work undergoes three significant stages: de-noising, segmentation, and resolution improvement of the noisy input image. The double density discrete wavelet transform (DDDWT) technique is introduced to remove the noise and improve the visual image’s quality. Finally, the image resolution and quality enhancement are done using CNN with overlap and add (OA)-FFT based Wallace tree multiplier-red deer optimization (WT-RDO) technique. In the experimental scenario, the quality enhancement and image resolution outcome achieved accuracy of 97 % and 98 %, precision of 97 % and 97 %, sensitivity of 96 % and 98 %, specificity of 96 % and 98 %, PSNR of 30 dB and 26 dB, structural similarity index (SSIM) of 0.86 and 0.85, mean square error (MSE) of 0.01, mean absolute error (MAE) of 0.8, the time complexity of 61, power of 0.08 W, delay of 0.6 ns and throughput of 3.2 MB/s.