# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 5 Conclusions  
The presence of noise in a medical image makes it difficult to identify and analyze diseases. The traditional approach experiences losses and causes over-smoothing, demanding greater computational requirements. Therefore, there is a requirement for optimization approaches for medical image denoising, and medical image denoising is essential as a pre-processing step for any further diagnosis process. In this paper, a novel digital filter for medical image denoising is proposed using hybrid cuckoo particle search optimization with a decimation box filter for restoration from noisy images. This paper presents the resulting ablation study on different optimization algorithms. For the evaluation of results, the input medical images were corrupted with different noises and then filtered through a designed hybrid-optimization-based guided decimation box filter. The optimal coefficients and degree of smoothing were obtained. The result was observed with a variance in the noise, input image size, and decimation box. Apart from this, to validate the designed filter, its comparison was performed on some existing approaches for image denoising. The results prove that the developed model exhibits better performance in terms of quantitative measures. In the future, this work will be extended to some real-time applications.  
Conceptualization, D.G., K.K. and B.P.S.; Methodology, D.G.; Software, D.G.; Validation, B.P.S.; Formal Analysis, K.K.; Investigation, D.G.; Resources, B.P.S.; Data Curation, D.G.; Writing--Original Draft Preparation, D.G.; Writing--Review and Editing, D.G.; Visualization, D.G.; Supervision, K.K. and B.P.S. All authors have read and agreed to the published version of the manuscript.  
There was no outside funding for this study.  
There are no studies by the author using human subjects or animals in this article.  
Not applicable.  
The databases are easily accessible and can be obtained through references mentioned in the data description subsection of the manuscript. The remaining contents and relevant details are included in the manuscript itself.  
The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Ref** & **Algorithm** & \
\begin{tabular}{l} **Application** \\ **Area** \\ \end{tabular} & **Image Type** & **Gaussian** & **Poisson** &
\begin{tabular}{l} **Salt and** \\ **Pepper** \\ \end{tabular} & **Speckle** \\ \hline
[12] &
\begin{tabular}{l} Backtracking Optimized \\ Search Algorithm \\ \end{tabular} & Medical & CT & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[13] &
\begin{tabular}{l} Enhanced Grasshopper \\ optimization \\ \end{tabular} & Medical & CT & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[14] & Firefly & Medical & CT, X-ray, MRI & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[15] &
\begin{tabular}{l} Dragonfly (DF) and \\ Modified Firefly \\ \end{tabular} & Medical & MRI and CT & \(\times\) & \(\times\) & \(\surd\) & \(\times\) \\ \hline
[16] &
\begin{tabular}{l} Domain Translation \\ \end{tabular} & Medical & PET & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[32] &
\begin{tabular}{l} DNCNN \\ \end{tabular} & General & - & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[33] &
\begin{tabular}{l} Modified Cuckoo \\ Search Algorithm \\ \end{tabular} & General & - & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline
[35] & DeepRED & Medical & PET & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ \hline Proposed HCPSO & Medical &
\begin{tabular}{l} CT, X-ray, MRI, \\ Ultrasound \\ \end{tabular} & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative state of the art for noise-handling adaptation.