# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
Some researchers have also presented hybrid bio-inspired algorithms [23; 24; 25; 26; 27] for image denoising. [PERSON] et al. [23] explored the use of a hybrid approach of bio-inspired algorithms such as cuckoo's search and curvelet transform. Curvelet transform was adopted to represent the sparse pixel information and edges. Then, the CS algorithm was used to optimize the denoising coefficients while preserving the structural and morphological information of the images. [PERSON] et al. [28] introduced the guided digital filter. Motivated by these filters, the researcher used the digital filter for medical images such as retinal images [29], MRI [30], and ultrasound images [31]. [PERSON] et al. [32] then presented a hybrid CNN model for removing Gaussian noise. [PERSON] et al. [33] presented a hybrid approach modified cuckoo search algorithm. [PERSON] and [PERSON] [34] also hybridized the cuckoo search algorithm with a bilateral filter for medical image denoising. [PERSON] et al. [35] presented PET image denoising using a deep-learning denoising method known as DeepRED. [PERSON] et al. [36] presented unsupervised deep learning using deep neural networks (DNN) for MR image denoising. [PERSON] et al. [37] used ant colony optimization (ACO) with two-dimensional discrete cosine transform for medical denoising. [PERSON] and [PERSON] proposed medical denoising using Bi-dimensional Empirical Mode Decomposition (BEMD) [38]. [PERSON] used a bilateral filter with a genetic algorithm (BF-GA) for MR image denoising [39]. [PERSON] et al. [40] used the linear minimum mean square error method (LMMSE) for principal component analysis (PCA) to remove noise from an image. Therefore, motivated according to the research contributions provided by researchers, this paper explored bio-inspired optimization algorithms to provide edge-preserved denoising for medical images as a feasible solution. In this regard, the key contributions of this paper are:  
* This paper presented a novel hybrid optimization-guided decimation box filter for image denoising filters. Till now, a decimation box filter has never been used for denoising and this leads to novelty. * The designed HCPSO is application independent approach that is capable of handling multiple noise types, such as Gaussian, Poisson, Speckle, and salt and pepper noises. * The use of HCPSO in noise removal ensures improved efficiency, as HCPSO facilitates optimization. * The resulting analysis shows the ablation study on convergence criteria of different bio-inspired optimization algorithms. This paper also presents a result analysis with the variable noise level and decimation box size. * The proposed algorithm has low computational complexity because it only involves basic arithmetic operations on the image pixels. Therefore, this paper aims to design a denoising filter for medical images using a novel decimation box filter guided using a bio-inspired optimization hybrid algorithm to reduce the error caused by different noises. The design of the filter and optimization algorithm is achieved in MATLAB. The complete framework is discussed. The proposed novel filter is tested on three different datasets and four different noises to show its better performance over other existing states-of-art denoising filters. The remaining paper is divided into the following sections: Section 2 provides a brief description of digital FIR filters and the application of bio-inspired optimization algorithms for designing digital filters. Additionally, different noises present in medical images are discussed. Section 3 presents the proposed hybrid model to design an optimization-based guided decimation box filter (GDBF) for medical image denoising. Section 4 presents the implementation details and the results of the analysis for the implemented model. This section also presents the ablation study and comparative state-of-the-art models. Finally, in Section 5, the conclusion and future scope of the paper are presented.